How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

Autoregressive large language models (LLMs) compress knowledge from their training data
through next-token conditional distributions. This limits tractable querying of this knowledge …

In this article we show how the problem of neural text generation can be constructively
reformulated in terms of transitions between the states of a finite-state machine. This …

In this article we describe an efficient approach to guiding language model text generation
with regular expressions and context-free grammars. Our approach adds little to no …

Diffusion models excel at capturing the natural design spaces of images, molecules, DNA,
RNA, and protein sequences. However, rather than merely generating designs that are …

Automatically generating novel and interesting games is a complex task. Challenges include
representing game rules in a computationally workable form, searching through the large …

People often give instructions whose meaning is ambiguous without further context,
expecting that their actions or goals will disambiguate their intentions. How can we build …

Code is increasingly becoming a core data modality of modern machine learning research
impacting not only the way we write code with conversational agents like OpenAI's ChatGPT …

We give a model of how to infer natural language rules by doing experiments. The model
integrates Large Language Models (LLMs) with Monte Carlo algorithms for probabilistic …

This tutorial provides an in-depth guide on inference-time guidance and alignment methods
for optimizing downstream reward functions in diffusion models. While diffusion models are …